Patrick Barker is the Founder / CTO of Kentauros AI.

Farhood Etaati is a Software Engineer at Yektanet.

MLOps podcast #204 with Patrick Barker, CTO of Kentauros AI and Farhood Etaati, MLOps/Platform Team Lead at AIMedic, MLOps at the Crossroads.

// Abstract

MLOps is at a crossroads. The ever-increasing excitement for LLMs' ability to solve some interesting real-world problems has made many people interested in applying these models in new applications which comes with its own challenges, that have upstarted the term "LLMLOps". But how much of those challenges are not a newer representation of what older-gen ML models had to deal with in the production, and the question arises whether developing "new" specialized tools to address these applications actually provides any substantial value for the sustainability of the field in general. Tools are coming and going at a rate that makes many technical people skeptical of adopting newer tools. What can we do as a community to alleviate these issues? Why OSS MLOps is lacking behind and how VC money is contributing to that?

// Bio

Farhood Etaati

MLOps engineer at AIMedic. Studied EE at Uni of Tehran, started out as a data scientist, and pivoted to software engineering. Currently working on on-premise MLOps platform development suitable for Iran's infrastructure.

Patrick Barker

When Patrick is not occupied with building his AI company, he enjoys spending time with his wonderful kids or exploring the hills of Boulder.

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// Related Links

Websites: https://github.com/pbarker

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Connect with Farhood on LinkedIn: www.linkedin.com/in/farhood-etaati

Timestamps:

[00:00] Farhood's and Patrick's preferred coffee

[01:13] Takeaways

[04:00] Please like, share, and subscribe to our MLOps channels!

[05:26] Strong feelings

[10:21] MLOps vs DevOps Challenges

[13:44] Medical setting, ML tools, NLP, model building

[16:23] MLOps vs Data Engineering

[20:45] MLOps Boosts LLM Development

[23:54] Longtail Use Cases

[31:00] Tech Roles Distinctions

[34:42] Did He Say That?

[37:04] Fine-tuning AI Models

[38:57] ML 2.0 Advancements Explained

[41:11] Generative AI in MLOps

[45:04] ML Reproducibility Challenges

[48:03] Wrap up